# Demographic
age <- read_csv(here("data", "TAG_age_session_dates_4waves.csv"))
age <- clean_names(age)
race <- read_excel(here("data", "TAG_W1_Race_Ethnicity.xlsx"))
race <- clean_names(race)
# Emotion Regulation(ERQ)
erq <- read_csv(here("data", "ERQ_Wave1.csv"))
erq <- erq %>%
clean_names() %>%
distinct(tagid, .keep_all = TRUE)
# Adolescent Depression(CESDC)
cesdc <- read_csv(here("data", "CESDC_Wave1.csv"))
cesdc <- clean_names(cesdc)We will be utilizing data from the longitudinal study “Transitions in Adolescent Girls,” (Barendse et al., 2020) specifically focusing on data from baseline (Wave 1). This dataset includes 174 participants who were 10-13 at the baseline. Our variables of interest are emotion regulation strategy use and depressive symptoms. These were measured using the Emotion Regulation Questionnaire (ERQ; John & Gross, 2003) and the Center for Epidemiological Studies Depression Scale for Children (CES- DC, Weissman et al., 1980). These are continuous scales which provide total scores for reappraisal and suppression (ERQ) and depressive symptoms (CES-DC).
Race/ethnicity category
Clean the data frame: identify subjects reported multiple racial categories and rename them to “Multi-racial”
Age
We have subject age data at both sessions and we are interested in calculating the mean age of the two sessions of wave1
df <- erq %>%
left_join(cesdc, by= 'tagid')
df <- df %>%
left_join(age, by = 'tagid')
df <- df %>%
left_join(race_clean, by = 'tagid') %>%
select(tagid, erq_reappraisal_total, erq_suppression_total, ces_dc_total_75perc, W1_mean_age, w1_ethnicity,
w1s1_date, w1s1_age, w1s2_date, w1s2_age, w2s1_date, w2s1_age, w2s2_date, w2s2_age,
w3s1_date, w3s1_age, w3s2_date, w3s2_age, w4s1_date, w4s1_age, w4s2_date, w4s2_age
)
df <- df %>%
rename(
wave1_session1_date = w1s1_date,
wave1_session1_age = w1s1_age,
wave1_session2_date = w1s2_date,
wave1_session2_age = w1s2_age,
wave2_session1_date = w2s1_date,
wave2_session1_age = w2s1_age,
wave2_session2_date = w2s2_date,
wave2_session2_age = w2s2_age,
wave3_session1_date = w3s1_date,
wave3_session1_age = w3s1_age,
wave3_session2_date = w3s2_date,
wave3_session2_age = w3s2_age,
wave4_session1_date = w4s1_date,
wave4_session1_age = w4s1_age,
wave4_session2_date = w4s2_date,
wave4_session2_age = w4s2_age
)# Tidy
df2 <- df %>%
mutate(across(starts_with("wave"), as.character))
df_long <- df2 %>%
pivot_longer(
cols = starts_with("wave"),
names_to = c("wave", "session", "info"),
names_sep = "_",
values_to = "value"
) %>%
mutate(
wave = parse_number(wave),
session = parse_number(session)
)
df_tidy <- df_long %>%
pivot_wider(
names_from = info,
values_from = value
)
# Filter
df_final <- df_tidy %>%
filter(wave == 1, session == 1)ggplot(df_tidy, aes(x = age, y = tagid)) +
geom_line(aes(group = tagid), size = .5, alpha = .4) +
geom_point(aes(color = as.factor(wave)), size = 2) +
labs(
x = "Age",
y = "TAG Participant",
title = "Age by Wave",
color = "Wave"
) +
theme_classic() +
theme(
legend.position = "top",
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)| Unique | Missing Pct. | Mean | SD | Min | Median | Max | Histogram | |
|---|---|---|---|---|---|---|---|---|
| Reappraisal | 31 | 2 | 27.4 | 6.6 | 6.0 | 28.0 | 41.0 | |
| Suppression | 24 | 2 | 13.7 | 5.2 | 5.0 | 13.0 | 28.0 | |
| Depression | 49 | 5 | 12.8 | 10.3 | 0.0 | 9.5 | 50.0 | |
| Age | 141 | 1 | 11.6 | 0.8 | 10.0 | 11.7 | 13.1 | |
| Ethnicity | N | % | ||||||
| a. Black/ African American | 8 | 0.7 | ||||||
| b. Hispanic/ Latino/ Chicano | 48 | 4.2 | ||||||
| c. Native American or Native Alaskan | 8 | 0.7 | ||||||
| d. White / Caucasian | 720 | 63.4 | ||||||
| e. Asian | 16 | 1.4 | ||||||
| g. Multi-racial | 248 | 21.8 | ||||||
| h. Other | 16 | 1.4 | ||||||
| j. Decline to respond | 8 | 0.7 | ||||||
| NA | 64 | 5.6 |
Is higher emotion reappraisal associated with lower depression symptoms in adolescent girls?
Is higher emotion suppression associated with higher depression symptoms in adolescent girls?
Is higher suppression use associated with higher depression symptoms when adjusting for reappraisal use?
ex1 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_suppression_total + df_tidy$erq_reappraisal_total)
check_model(ex1)Does age moderate the relation between reappraisal/suppression and depression symptoms in adolescent girls?
Primary RQ Tables
modelsummary(
list(
"Reappraisal" = rq1,
"Suppression" = rq2),
coef_map = c(
"(Intercept)" = "Intercept",
"df_tidy$erq_reappraisal_total" = "Reappraisal (ERQ)",
"df_tidy$erq_suppression_total" = "Suppression (ERQ)"
),
stars = TRUE,
title = "Table 2. Model Summary for RQ1 and RQ2"
)| Reappraisal | Suppression | |
|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||
| Intercept | 22.805*** | 3.864*** |
| (1.306) | (0.800) | |
| Reappraisal (ERQ) | -0.362*** | |
| (0.046) | ||
| Suppression (ERQ) | 0.645*** | |
| (0.055) | ||
| Num.Obs. | 1056 | 1056 |
| R2 | 0.055 | 0.114 |
| R2 Adj. | 0.054 | 0.113 |
| AIC | 7890.8 | 7719.0 |
| BIC | 7905.7 | 7733.9 |
| Log.Lik. | -3942.414 | -3856.517 |
| F | 60.906 | |
| RMSE | 10.12 | 9.33 |
modelsummary(
list(
"Suppression + Reappraisal" = ex1,
"Reappraisal × Age Interaction" = ex2,
"Suppression × Age Interaction" = ex3),
coef_map = c(
"(Intercept)" = "Intercept",
"df_tidy$erq_reappraisal_total" = "Reappraisal (ERQ)",
"df_tidy$erq_suppression_total" = "Suppression (ERQ)",
"df_tidy$erq_reappraisal_total:df_tidy$W1_mean_age" = "Reappraisal x Age",
"df_tidy$erq_suppression_total:df_tidy$W1_mean_age" = "Suppression x Age"),
stars = TRUE,
title = "Table 3. Model Summaries for Multiple Regression and Interaction Models"
)| Suppression + Reappraisal | Reappraisal × Age Interaction | Suppression × Age Interaction | |
|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | |||
| Intercept | 13.733*** | 40.986* | 2.701 |
| (1.484) | (18.413) | (11.043) | |
| Reappraisal (ERQ) | -0.332*** | -1.688** | |
| (0.043) | (0.644) | ||
| Suppression (ERQ) | 0.592*** | 0.053 | |
| (0.055) | (0.743) | ||
| Reappraisal x Age | 0.116* | ||
| (0.056) | |||
| Suppression x Age | 0.049 | ||
| (0.064) | |||
| Num.Obs. | 1032 | 1056 | 1056 |
| R2 | 0.164 | 0.075 | 0.119 |
| R2 Adj. | 0.162 | 0.073 | 0.117 |
| AIC | 7504.3 | 7871.6 | 7716.9 |
| BIC | 7524.1 | 7896.4 | 7741.7 |
| Log.Lik. | -3748.160 | -3930.786 | -3853.466 |
| F | 28.523 | ||
| RMSE | 9.14 | 10.01 | 9.30 |